Paper, Int. J. Elect. Enging. Educ., Vol. 36, pp. 139–<br/>153. Manchester U.P., 1999. Printed in Great Britain<br/>In this paper, an attractive approach for teaching genetic algorithm (GA) is presented.<br/>This approach is based primarily on using MATLAB in implementing the genetic operators:<br/>
Genetic Algorithm: An Approach for Optimization (Using MATLAB)
✍ Scribed by Subhadip S.
- Tongue
- English
- Leaves
- 7
- Category
- Library
No coin nor oath required. For personal study only.
✦ Synopsis
Paper, 7 p, International Journal of Latest Trends in Engineering and Technology, Vol. 3 Issue 3 January 2014.
In this paper we have gone through a very brief idea on Genetic Algorithm, which is a very new approach for problems related to Optimization. There are many techniques used to optimize a function but in case of optimizing Multimodal Functions most of these techniques face a common problem of robustness. This can be overcome by using Genetic algorithm. Through this paper we will learn how the Genetic Algorithm actually works with proper explanation and with some real time examples based on MATLAB.
✦ Subjects
Информатика и вычислительная техника;Искусственный интеллект;Эволюционные алгоритмы
📜 SIMILAR VOLUMES
<p>The book is a monograph in the cross disciplinary area of Computational Intelligence in Finance and elucidates a collection of practical and strategic Portfolio Optimization models in Finance, that employ Metaheuristics for their effective solutions and demonstrates the results using MATLAB imple
Book, 64 p, March 2002<br/>Contents<br/>Optimization and hill climbing<br/>The simplex method<br/>Iterated simplex<br/>A set of test problems<br/>Performance of the simplex and iterated simplex methods<br/>Evolution optimization and genetic algorithms<br/>Biological evolution<br/>The power of cumula
Paper, 12 p, International journal of optimization in civil engineering, 2012.<br/>Equipment selection is a key factor in modern construction industry. As it is a complex factor, current models offered by literatures fail to provide adequate solutions for major issues like systematic evaluation of s
This book, developed through class instruction at MIT over the last 15 years, provides an accessible, concise, and intuitive presentation of algorithms for solving convex optimization problems. It relies on rigorous mathematical analysis, but also aims at an intuitive exposition that makes use of vi
<p><P>Network models are critical tools in business, management, science and industry. <EM>Network Models and Optimization: Multiobjective Genetic Algorithm Approach</EM> presents an insightful, comprehensive, and up-to-date treatment of multiple objective genetic algorithms to network optimization